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---
license: cc-by-4.0
datasets:
- CS2CD/Context_window_256
metrics:
- accuracy
- roc_auc
- recall
- precision
- f1
tags:
- transformer
- game
- Counter-Strike2
- CS2
- counter-strike
- Cheat-detection
---
# AntiCheatPT_256
This Model is the best performing transformer-based model from the thesis:
AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive
Computer Games by Mille Mei Zhen Loo & Gert Luzkov.
The thesis can be found [here](https://github.com/Pinkvinus/CS2_cheat_detection/blob/main/AntiCheatPT%20A%20Transformer-Based%20Approach%20to%20Cheat%20Detection%20in%20Competitive%20Computer%20Games.pdf)
**Code:** [Here](https://github.com/Pinkvinus/CS2_cheat_detection/tree/main/Transformer)
## Results
| Metric | Value |
|-------------|--------|
| Accuracy | 0.8917 |
| ROC AUC | 0.9336 |
| Precision | 0.8513 |
| Recall | 0.6313 |
| Specificity | 0.9678 |
| F1 | 0.7250 |
## Model architecture
| **Component** | **Value** |
|-----------------------------------|-----------------------------------------|
| Context window size | 256 |
| Transformer layers | 4 |
| Attention heads | 1 |
| Transformer feedforward dimension | 176 |
| Loss function | Binary Cross Entropy (BCEWithLogitLoss) |
| Optimiser | AdamW (learning rate = 10<sup>-4</sup>) |
| Scheduler | StepLR (gamma = 0.5, step size = 10) |
| Batch size | 128 |
## Data
The input data used for this model was the [Context_window_256](https://huggingface.co/datasets/CS2CD/Context_window_256) dataset based on the [CS2CD](https://huggingface.co/datasets/CS2CD/CS2CD.Counter-Strike_2_Cheat_Detection) dataset.
## Model testing
Various validation metrics of training can be seen below:
The model confusion matrix on test data can be seen below:
## Usage notes
- The dataset is formated in UTF-8 encoding.
- Researchers should cite this dataset appropriately in publications.
## Application
- Cheat detection
## Acknowledgements
A big heartfelt thanks to [Paolo Burelli](http://paoloburelli.com/) for supervising the project.
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